Read rope_theta from rope_parameters in the Llama injection policy

[alanhuangyoo]

Fixes #8340.

What breaks

DS_LLAMAContainer.create_module resolves rope_theta as:

if hasattr(self.policy.client_module.self_attn, 'config'):
    _config.rope_theta = self.policy.client_module.self_attn.config.rope_theta
else:
    _config.rope_theta = self.policy.client_module.self_attn.rope_theta

transformers 5.0 folded the rotary settings into config.rope_parameters and dropped the attribute. LlamaAttention still has .config, so the first branch is taken and it raises.

This is not specific to the DeepSeek checkpoint in the issue — on transformers >= 5.0 it reproduces with a default LlamaConfig. Checked on 5.8.0:

transformers 5.8.0
LlamaConfig().rope_theta        -> AttributeError
LlamaConfig().rope_parameters   -> {'rope_theta': 10000.0, 'rope_type': 'default'}

and against a real module, both branches are dead:

self_attn has .config                 True
self_attn has .rope_theta             False
self_attn.config has rope_theta       False
self_attn.config has rope_parameters  True

current code -> AttributeError: 'LlamaConfig' object has no attribute 'rope_theta'

The fix

Try the old spellings first, then rope_parameters['rope_theta'], so pre-5.0 installs take exactly the path they take today and nothing changes for them.

This is the same drift #7443 adapted to. The num_heads accessor it fixed alongside still resolves on 5.8 (config.num_attention_heads is intact), so rope_theta is the only one that moved again.

requirements-dev.txt asks for transformers>=4.51.3 with no upper bound, so 5.x is in range.

Verified that rope_theta is the only accessor that moved: against a real LlamaDecoderLayer on 5.8.0, get_hidden_heads(), attention(), mlp() and layernorm() all resolve once this one is fixed.

I exercised the policy and container layer, not a full init_inference() run against a downloaded checkpoint on GPU.

Test

tests/unit/module_inject/test_llama_rope_theta.py covers the three layouts plus the precedence between them and the not-found case — CPU only, no model download:

6 passed

End to end against a real LlamaAttention on 5.8.0:

current code   AttributeError: 'LlamaConfig' object has no attribute 'rope_theta'
with this PR   10000.0

tests/unit/module_inject/    43 passed
yapf --diff / flake8         clean

I did not touch inference/v2. Six of its model implementations read self._config.rope_theta directly and exaone4 is the only one using a getattr default, so they likely have the same exposure — but that is a different engine and a different change, and I have not reproduced it.

[alanhuangyoo]

Opened #8345 for the inference/v2 exposure I flagged at the bottom of this description — separate engine, separate change, so I kept it out of here.

It turned out to be eight models, and one of them fails quietly rather than loudly: exaone4 reads the base through getattr(self._config, "rope_theta", 1000000.0), and Exaone4Config carries rope_parameters["rope_theta"] == 10000.0, so on 5.x it puts a 100x rotary base into RotateHalfConfig with nothing raised.